Automate AMI Sorting for Your Affordable Housing Waitlist

Property management systems like RealPage and AppFolio are great for lease management but their affordable housing modules fall short on automation. They require leasing teams to manually read paystubs, calculate anticipated 12-month income, and then select the correct AMI percentage from a dropdown menu. This workflow turns trained leasing agents into expensive data entry clerks and creates significant compliance risk.

A single mistake in these manual calculations can have serious consequences. Consider a leasing agent processing 30 applications for a 500-unit property. They misinterpret a bonus structure on a paystub, calculating an applicant's annual income at $42,000 instead of $45,000. This places the applicant in the 50% AMI tier instead of the correct 60% AMI tier. The error isn't caught for weeks, wasting everyone's time and potentially violating fair housing rules if the unit goes to someone further down the waitlist.

This manual process does not scale, especially during a lease-up where hundreds of applications arrive at once. The bottleneck isn't the software; it's the human-powered, error-prone process of turning income documents into a single, accurate number. Without automation, property managers are forced to either hire more staff or accept a slow, risky application review process.

Syntora's approach would involve engineering a dedicated service tailored to your operational environment, integrating directly with RealPage or AppFolio. The initial phase would include a detailed audit of your current application submission process and compliance requirements to inform the architecture.

Upon an applicant submitting an application, a webhook would trigger an AWS Lambda function, passing the applicant data and attached income documents. This event-driven architecture is designed for near-instant processing, avoiding processing queues. The Lambda function would route income documents (PDFs, JPEGs of paystubs, W-2s) to the Claude API. Syntora would craft and refine prompts to precisely extract key data points such as hourly wage, hours per week, YTD earnings, overtime, tips, and bonuses.

A custom Python script would then execute your specific income anticipation logic, annualizing wages (e.g., hours x 2080) and projecting other income sources over the next 12 months, adhering to your jurisdictional rules. For similar document processing workloads, this parsing and calculation workflow typically completes within a couple of minutes.

Once the anticipated annual income is calculated, the system would query a Supabase database. This database would house the current year's AMI tables specific to your properties, which we would populate and maintain. The custom solution would match the applicant's income and household size to the correct AMI bucket (e.g., 30%, 50%, 60%). Finally, it would use the RealPage or AppFolio API to write this AMI percentage directly into a custom field on the applicant's record. Updates via these APIs are generally reflected in a Property Management System within a few seconds.

The delivered system would integrate directly into your existing PMS, enabling your leasing team to filter waitlists by the auto-populated AMI field. This allows efficient applicant sorting for specific AMI-restricted units. Depending on requirements, the solution could also include configuration for automated email confirmations to applicants, streamlining communication and reducing manual follow-up. Typical build timelines for this complexity range from 8 to 16 weeks, depending on the number of integration points and custom rule sets. Clients would need to provide detailed documentation of their compliance rules and access to their PMS APIs.